As the saying goes, “there are many fish in the sea,” but how many exactly? Or how many amphibians are in our National Parks, seabirds in the Atlantic Ocean or monarch butterflies fluttering across North America? More importantly, how can we determine if their numbers are increasing or decreasing or if they are moving to new areas and expanding their ranges?

Although these questions are seemingly straightforward, they cannot readily beanswered through simple observation. Solving these puzzles requires complex statistical and mathematical models.

Research in my Quantitative Ecology Lab aims to identify climate and habitat factors responsible for variations in species distributions and abundances. Our goal is to understand how climate and environmental change impact populations and communities.

With this information, we can develop conservation strategies to protect threatened species and control invasive ones. We research a wide range of taxa – amphibians, birds, fish, insects and mammals – but the common link is quantitative modeling.

One of my current projects involving the U.S. National Park Service shows that 8 out of 12 wetland-breeding amphibian species are declining in one particular park in the National Capital Region of the U.S. parks system. It turns out that hydroperiod – the length of time that a wetland holds water within a season – is the major predictor of whether individual wetlands within the park can sustain populations. Climate change predictions suggest that hydroperiod is likely to decrease as a result of changes in precipitation in the mid-Atlantic, something that would be catastrophic for the amphibians that rely on wetlands to breed.

We developed hierarchical community models to devise a conservation strategy that focuses on increasing the hydroperiod for wetlands within the park. Our model determined which wetlands are likely to be most receptive to management based on their locations in the landscape, size, and current species compositions.

Now we are looking at extending our approach to other parks in the region. Given that every park has slightly different conservation needs, our objective is to devise strategies that meet individual park goals, but also successfully protect species at the regional level.

Our lab is currently developing statistical models for another project that I’ve been involved with for several years: wind energy development in the nearshore Atlantic. My graduate student, Allison Sussman, and I are synthesizing data to determine the optimal placement of wind farms and the effects that wind energy development will have on marine avian species. Although public interest has focused on the collision risk for birds that fly through wind farms, we also need to learn how wind energy could impact the viability of seabird populations.

I am especially interested in combining different types of data to answer large-scale questions using integrated population models. To that end, our lab has been collaborating on a project modeling monarch populations across North America. Citizen scientists across the country have been collecting data through the North American Butterfly Association’s annual “July 4” counts, while other groups have been tracking monarchs more intensely at particular locations for many years.

Monarch populations have been declining, particularly over the past few years. As a result, the monarch is currently under consideration for listing as an endangered species. But why are monarchs declining? This is still unclear. Is it loss of milkweed during the summer breeding season or loss of available habitat on the overwintering grounds? Increases in parasitoids? Climate change? Some combination of these variables? Our models will help tease apart the specific factors contributing to population declines. Combining all of the available data enables us to learn much more about this iconic butterfly.

The world is filled with so many difficult ecological and conservation questions that ultimately affect all of us. The goal in my lab is to address these challenges by advancing modeling techniques that can give us answers and lead to solutions for even the mostdaunting problems.